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Laboratory of Applied Economics

Code 14812
Year 3
Semester S2
ECTS Credits 6
Workload TP(60H)
Scientific area Economics
Entry requirements Not applicable.
Mode of delivery Face-to-face
Work placements Not applicable
Learning outcomes • To train students in the advanced use of spreadsheet for presentation and transformation of data.
• To empower students for the manipulation of large volumes of data.
• To empower students to use data analysis and visualization software (Excel, Power BI and R).
• To empower students with the clear and effective presentation of data driven insights.
Syllabus 1 - Data types and structure
2 - Introduction to databases
3 - Data collection
4 - Data preparation
5 - Data transformation
6 - Data visualization
7 - Extraction of data information
Main Bibliography Hadley Wickham, Mine Çetinkaya-Rundel and Garrett Grolemund, R for Data Science (2e), 2023
Aspin, Adam, Pro Power BI Desktop, Apress, 2nd edition, 2018
Guerrero, Hector,. Excel Data Analysis: modeling and simulation, Spinger, 2nd edition, 2019.
Provost, Foster e Fawcett, Tom,. Data Science for Business: What you need to know about data mining and data-analytic thinking, 1str edition, 2013
Teaching Methodologies and Assessment Criteria The students' classification corresponds to the weighting of three assessment moments:
• First exam 20% - 21 March
• Second exam 25% - 29 April
• Second exam 25% - DGE Evaluation Week
• Project 30%
Project Delivery 20% - 15 April
Presentation 10% – 17 April
Language Portuguese. Tutorial support is available in English.
Last updated on: 2024-06-12

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